Every semester
10 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
Students acquire advanced theoretical-methodological knowledge in the areas of artificial intelligence and machine learning, which are necessary for the implementation of intelligent, adaptive behavior and the interaction capability of technical systems. After completing the module, students are able to apply modern data- or model-based methods of AI/machine learning (e.g. deep learning, reinforcement learning, probabilistic models, XAI). In the first part of the module, theoretical-methodical knowledge on one of the topics of Advanced Artificial Intelligence is acquired and practiced in accompanying exercises. In the second module part, these methods will be deepened in a seminar. For this purpose, you will have acquired both theoretical-methodical knowledge and the competence to deal with a topic independently (research, evaluation, implementation, oral presentation, and written discussion).
The module provides in-depth theoretical and methodological knowledge of artificial intelligence necessary for the development of intelligent interactive systems. The teaching content of the module includes e.g. basic courses from the areas of machine learning, artificial intelligence, deep learning, reinforcement learning, XAI, cognitive computing, models of decision-making, neural networks, auditory data science, interactive and autonomous learning. The courses chosen by the student determine the specific course content of the module. Selection from the range of courses designated for this purpose will be based on personal interest.
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The courses include an introductory lecture (2 CP) with the corresponding exercise (2 CP) + a seminar (2 CP) with the corresponding exercise (2 CP) from a related subject area.
OR
The courses include an introductory lecture (2 CP) with the corresponding exercise (2 CP) + an in-depth lecture (2 CP) with the corresponding exercise (2 CP) from a related subject area.
Reasoning of the necessity of two partial exams:
Combination seminar + lecture:
Two partial examinations are necessary since the theoretical and mathematical competencies are tested in the written/oral examination and methodological knowledge as well as the competence of presenting and written examination of a topic are tested in the seminar.
Combination of lecture + lecture:
Two partial examinations are necessary, as the first partial examination tests the respective theoretical and content-related competences and the second partial examination tests the subsequent methodological and/or in-depth knowledge.
Module structure: 2 bPr 1
To study together with a corresponding exercise from the field of Advanced Artificial Intelligence.
To study together with a corresponding lecture or with the associated seminar, each from the field of Advanced Artificial Intelligence.
To study together with a corresponding lecture or with the associated seminar, each from the field of Advanced Artificial Intelligence.
To study together with a corresponding exercise from the field of Advanced Artificial Intelligence.
To study together with a corresponding exercise from the field of Advanced Artificial Intelligence.
To study together with a corresponding lecture or with the associated seminar, each from the field of Advanced Artificial Intelligence.
To study together with a corresponding lecture or with the associated seminar, each from the field of Advanced Artificial Intelligence.
Partial Examination 1 (Lecture + Exercise)
Portfolio with final examination consisting of:
1) Portfolio of exercises related to the content of the lecture
Exercise tasks or programming tasks that are assigned in relation to the course (passing threshold: 50% of the achievable points). The assessment of the exercise tasks also includes direct questions regarding the solutions that must be answered by the students during the exercises. The instructor may require an individual explanation and demonstration of tasks and can replace a portion of the exercise tasks with in-person exercises. The exercise tasks within the portfolio are generally assigned weekly and serve to support the independent learning of implementations of the content presented in the seminar/in the lecture. Further specification, particularly regarding the time frame of the final examination, will be provided in the course description.
2) A final examination for the lecture
The final examination regarding the content of the lecture refers to the exercise or programming tasks or develops from the competencies learned in the exercises.
Lecture: Final exam (lasting 90-120 minutes) or oral final examination (lasting 20-30 minutes) covering the content conveyed in the lecture and developed in the exercises.
The exam can alternatively be conducted as an e-exam, open book exam, or e-open book exam. In the case of open book and e-open book exams, the duration is 120-150 minutes.
Both portfolio elements will be assessed by an examiner. A final overall assessment will be provided.
Partial Examination 2 upon Completion of Seminar + Exercise
Portfolio with final examination consisting of:
1) Portfolio of exercises related to the content of the seminar
Exercise tasks or programming tasks that are assigned in relation to the course (passing threshold: 50% of the achievable points). The assessment of the exercise tasks also includes direct questions regarding the solutions that must be answered by the students during the exercises. The instructor may require an individual explanation and demonstration of tasks and can replace a portion of the exercise tasks with in-person exercises. The exercise tasks within the portfolio are generally assigned weekly and serve to support the independent learning of implementations of the content presented in the seminar/in the lecture. Further specification, particularly regarding the time frame of the final examination, will be provided in the course description.
2) A final examination for the seminar
The final examination regarding the content of the seminar refers to the exercise or programming tasks or develops from the competencies learned in the exercises.
Seminar: Presentation (lasting 30–40 minutes) with written report (10-15 pages)
The students present, after coordinating the specific task with the examiner, the significance and systematic-scientific classification of a problem addressed in the seminar and explain and present their topic in writing in their report, incorporating aspects from the discussion in the seminar. The task may also include the elaboration of an application (i.e., programming/calculation, etc.) of a method to a typically practically significant individual case. The presentation with report refers to the content conveyed in the seminar and developed in the exercises.
Both portfolio elements will be assessed by an examiner. A final overall assessment will be provided.
Partial Examination 2 upon Completion of Lecture + Exercise
Portfolio with final examination consisting of:
1) Portfolio of exercises related to the content of the lecture
Exercise tasks or programming tasks that are assigned in relation to the course (passing threshold: 50% of the achievable points). The assessment of the exercise tasks also includes direct questions regarding the solutions that must be answered by the students during the exercises. The instructor may require an individual explanation and demonstration of tasks and can replace a portion of the exercise tasks with in-person exercises. The exercise tasks within the portfolio are generally assigned weekly and serve to support the independent learning of implementations of the content presented in the seminar/in the lecture. Further specification, particularly regarding the time frame of the final examination, will be provided in the course description.
2) A final examination for the lecture
The final examination regarding the content of the lecture refers to the exercise or programming tasks or develops from the competencies learned in the exercises.
Lecture: Final exam (lasting 90-120 minutes) or oral final examination (lasting 20-30 minutes) covering the content conveyed in the lecture and developed in the exercises.
The exam can alternatively be conducted as an e-exam, open book exam, or e-open book exam. In the case of open book and e-open book exams, the duration is 120-150 minutes.
Both portfolio elements will be assessed by an examiner. A final overall assessment will be provided.
| Degree programme | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|
| Intelligent Interactive Systems / Master of Science [FsB vom 16.05.2023 mit Änderungen vom 15.12.2023 und 01.04.2025 und Berichtigung vom 16.07.2024] | 2. o. 3. | one or two semesters | Compulsory optional subject |
The system can perform an automatic check for completeness for this module.
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